Set ccache size before building (#2570)

* Add type annotations to mlx methods

* Missing list_or_scalar

.circleci/config.yml

@@ -230,6 +230,9 @@ jobs:
             sudo mv ccache-4.11.3-linux-x86_64/ccache /usr/bin/ccache
             rm -rf ccache-4.11.3-linux-x86_64
             curl -LsSf https://astral.sh/uv/install.sh | sh
+      - run:
+          name: Set CCache size
+          command: ccache --max-size 1G
       - run:
           name: Install Python package
           command: |
@@ -260,7 +263,6 @@ jobs:
           command: |
             ccache --show-stats
             ccache --zero-stats
-            ccache --max-size 400MB
             ccache --cleanup
       - save_cache:
           key: cuda-<< parameters.image_date >>-{{ arch }}-{{ epoch }}

docs/src/usage/indexing.rst

@@ -107,8 +107,20 @@ same array:
   >>> a
   array([1, 2, 0], dtype=int32)
 
+Note that unlike NumPy, slicing an array creates a copy, not a view. So
+mutating it does not mutate the original array:
 
-Note, unlike NumPy, updates to the same location are nondeterministic:
+.. code-block:: shell
+
+  >>> a = mx.array([1, 2, 3])
+  >>> b = a[:]
+  >>> b[2] = 0
+  >>> b
+  array([1, 2, 0], dtype=int32)
+  >>> a
+  array([1, 2, 3], dtype=int32)
+
+Also unlike NumPy, updates to the same location are nondeterministic:
 
 .. code-block:: shell
 

mlx/backend/metal/scaled_dot_product_attention.cpp

@@ -394,7 +394,7 @@ void ScaledDotProductAttention::eval_gpu(
 
   // Define some copy functions to ensure the layout of the inputs is as
   // expected.
-  copies.reserve(3);
+  copies.reserve(inputs.size());
   auto copy_unless = [&copies, &s](
                          auto predicate, const array& arr) -> const array& {
     if (!predicate(arr)) {

python/mlx/_stub_patterns.txt

@@ -1,20 +1,34 @@
+mlx.core.__prefix__:
+  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+  import sys
+  if sys.version_info >= (3, 10):
+    from typing import TypeAlias
+  else:
+    from typing_extensions import TypeAlias
+
+mlx.core.__suffix__:
+  from typing import Union
+  scalar: TypeAlias = Union[int, float, bool]
+  list_or_scalar: TypeAlias = Union[scalar, list["list_or_scalar"]]
+  bool_: Dtype = ...
+
 mlx.core.distributed.__prefix__:
-  from mlx.core import array, Dtype, Device, Stream
+  from mlx.core import array, Dtype, Device, Stream, scalar
   from mlx.core.distributed import Group
   from typing import Sequence, Optional, Union
 
 mlx.core.fast.__prefix__:
-  from mlx.core import array, Dtype, Device, Stream
+  from mlx.core import array, Dtype, Device, Stream, scalar
   from typing import Sequence, Optional, Union
 
 mlx.core.linalg.__prefix__:
-  from mlx.core import array, Dtype, Device, Stream
+  from mlx.core import array, Dtype, Device, Stream, scalar
   from typing import Sequence, Optional, Tuple, Union
 
 mlx.core.metal.__prefix__:
-  from mlx.core import array, Dtype, Device, Stream
+  from mlx.core import array, Dtype, Device, Stream, scalar
   from typing import Sequence, Optional, Union
 
 mlx.core.random.__prefix__:
-  from mlx.core import array, Dtype, Device, Stream
+  from mlx.core import array, Dtype, Device, Stream, scalar, float32, int32
   from typing import Sequence, Optional, Union

python/mlx/optimizers/optimizers.py

@@ -556,7 +556,7 @@ class AdamW(Adam):
         eps (float, optional): The term :math:`\epsilon` added to the
           denominator to improve numerical stability. Default: ``1e-8``
         weight_decay (float, optional): The weight decay :math:`\lambda`.
-          Default: ``0``.
+          Default: ``0.01``.
         bias_correction (bool, optional): If set to ``True``, bias correction
           is applied. Default: ``False``
     """

python/src/array.cpp

@@ -320,6 +320,7 @@ void init_array(nb::module_& m) {
       .def_prop_ro(
           "shape",
           [](const mx::array& a) { return nb::cast(a.shape()); },
+          nb::sig("def shape(self) -> tuple[int, ...]"),
           R"pbdoc(
           The shape of the array as a Python tuple.
 
@@ -347,6 +348,7 @@ void init_array(nb::module_& m) {
       .def(
           "item",
           &to_scalar,
+          nb::sig("def item(self) -> scalar"),
           R"pbdoc(
             Access the value of a scalar array.
 
@@ -356,6 +358,7 @@ void init_array(nb::module_& m) {
       .def(
           "tolist",
           &tolist,
+          nb::sig("def tolist(self) -> list_or_scalar"),
           R"pbdoc(
             Convert the array to a Python :class:`list`.
 

python/src/linalg.cpp

@@ -447,6 +447,8 @@ void init_linalg(nb::module_& parent_module) {
       "a"_a,
       nb::kw_only(),
       "stream"_a = nb::none(),
+      nb::sig(
+          "def eig(a: array, *, stream: Union[None, Stream, Device] = None) -> Tuple[array, array]"),
       R"pbdoc(
         Compute the eigenvalues and eigenvectors of a square matrix.
 
@@ -523,6 +525,8 @@ void init_linalg(nb::module_& parent_module) {
       "UPLO"_a = "L",
       nb::kw_only(),
       "stream"_a = nb::none(),
+      nb::sig(
+          "def eigh(a: array, UPLO: str = 'L', *, stream: Union[None, Stream, Device] = None) -> Tuple[array, array]"),
       R"pbdoc(
         Compute the eigenvalues and eigenvectors of a complex Hermitian or
         real symmetric matrix.

python/src/ops.cpp

@@ -4271,7 +4271,7 @@ void init_ops(nb::module_& m) {
       nb::kw_only(),
       "stream"_a = nb::none(),
       nb::sig(
-          "def dequantize(w: array, /, scales: array, biases: Optional[array] = = None, group_size: int = 64, bits: int = 4, mode: str = 'affine', *, stream: Union[None, Stream, Device] = None) -> array"),
+          "def dequantize(w: array, /, scales: array, biases: Optional[array] = None, group_size: int = 64, bits: int = 4, mode: str = 'affine', *, stream: Union[None, Stream, Device] = None) -> array"),
       R"pbdoc(
         Dequantize the matrix ``w`` using quantization parameters.
 

python/src/random.cpp

@@ -171,7 +171,7 @@ void init_random(nb::module_& parent_module) {
       "key"_a = nb::none(),
       "stream"_a = nb::none(),
       nb::sig(
-          "def normal(shape: Sequence[int] = [], dtype: Optional[Dtype] = float32, loc: Optional[scalar, array] = None, scale: Optional[scalar, array] = None, key: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"),
+          "def normal(shape: Sequence[int] = [], dtype: Optional[Dtype] = float32, loc: Union[scalar, array, None] = None, scale: Union[scalar, array, None] = None, key: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"),
       R"pbdoc(
         Generate normally distributed random numbers.
 

python/tests/test_fast_sdpa.py

@@ -619,6 +619,17 @@ class TestSDPA(mlx_tests.MLXTestCase):
         out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask)
         self.assertTrue(mx.allclose(ref, out, atol=1e-4, rtol=1e-4))
 
+    def test_sdpa_noncontiguous_inputs(self):
+        mask = mx.ones(shape=(4, 1, 7, 7), dtype=mx.bool_)
+        mx.random.seed(0)
+        q = mx.random.normal(shape=(4, 7, 32, 64)).swapaxes(1, 2)
+
+        k = mx.random.normal(shape=(4, 7, 8, 64)).swapaxes(1, 2)
+        v = mx.random.normal(shape=(4, 7, 8, 64)).swapaxes(1, 2)
+        out = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0, mask=mask)
+        ref = mlx_ref_attn(q, k, v, scale=1.0, mask=mask)
+        self.assertTrue(mx.allclose(ref, out, atol=1e-4, rtol=1e-4))
+
     def test_sdpa_promote_mask(self):
         mask = mx.array(2.0, mx.bfloat16)
         D = 64

setup.py

@@ -176,10 +176,6 @@ class GenerateStubs(Command):
         # Run again without recursive to specify output file name
         subprocess.run(["rm", f"{out_path}/mlx.pyi"])
         subprocess.run(stub_cmd + ["-o", f"{out_path}/__init__.pyi"])
-        # mx.bool_ gets filtered by nanobind because of the trailing
-        # underscore, add it manually:
-        with open(f"{out_path}/__init__.pyi", "a") as fid:
-            fid.write("\nbool_: Dtype = ...")
 
 
 class MLXBdistWheel(bdist_wheel):